def run_image(self, image_name, scale, angle, workspace):
'''Run measurements on image'''
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
pixel_data = image.pixel_data
image_labels = np.ones(pixel_data.shape, int)
if image.has_mask:
image_labels[~image.mask] = 0
scale_i, scale_j = self.get_angle_ij(angle, scale)
for name, value in zip(
F_HARALICK,
Haralick(pixel_data, image_labels, scale_i, scale_j).all()):
statistics += self.record_image_measurement(
workspace, image_name,
str(scale) + "_" + H_TO_A[angle], name, value)
return statistics
def run_one(self, image_name, object_name, scale, angle, workspace):
"""Run, computing the area measurements for a single map of objects"""
statistics = []
image = workspace.image_set.get_image(image_name,
must_be_grayscale=True)
objects = workspace.get_objects(object_name)
pixel_data = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = None
labels = objects.segmented
try:
pixel_data = objects.crop_image_similarly(pixel_data)
except ValueError:
#
# Recover by cropping the image to the labels
#
pixel_data, m1 = cpo.size_similarly(labels, pixel_data)
if np.any(~m1):
if mask is None:
mask = m1
else:
mask, m2 = cpo.size_similarly(labels, mask)
mask[~m2] = False
if np.all(labels == 0):
for name in F_HARALICK:
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, np.zeros((0, )))
else:
scale_i, scale_j = self.get_angle_ij(angle, scale)
for name, value in zip(
F_HARALICK,
Haralick(pixel_data, labels, scale_i, scale_j,
mask=mask).all()):
statistics += self.record_measurement(
workspace, image_name, object_name,
str(scale) + "_" + H_TO_A[angle], name, value)
return statistics
